Images comprised of data having grayscale level values are known in the art. As used herein, grayscale-based images shall be understood to comprise images where the value of each pixel is a single value which will ultimately be interpreted by some rendering platform as values (such as intensities) to be displayed (or analyzed). Displayed images of this sort are typically composed of shades of gray (hence the moniker “grayscale”) although any color (or, indeed, different colors) can serve in this regard. For any particular grayscale standard, there is a given available range of grayscale level values. For the sake of illustration, for example, a given grayscale standard might provide for one hundred different values which represent a range of black at the weakest intensity to white at the strongest intensity or, as another example, blue at the weakest intensity to red at the strongest intensity.
In some application settings, the use of grayscale-based images can yield, in the first instance, images having relatively indistinct features. A typical radiographic image, when rendered as a grayscale-based image, can have a high dynamic range that makes fine detail very difficult to see in all regions of a single rendered image. In many cases, such fine detail is discernable in bright regions of the image, or in dark regions, but not in both simultaneously.
Adaptive histogram equalization comprises a known technique to address this concern. By this approach, one divides the image into (typically overlapping) blocks. For each block, one then calculates the histogram of the block's grayscale levels; that is, how many times each grayscale level occurs in each block. An optimal non-linear equalization function is then derived from this histogram and applied to the block's grayscale levels. This function is often derived so that if the histogram of the processed block were calculated, the result would be very close to flat (such that all grayscale levels are used approximately the same number of times). The function is applied to each block and the results from overlapping blocks are then averaged together.
Unfortunately, such an approach is not necessarily appropriate or useful in all application settings. This approach, for example, tends to be computationally intensive as it requires that a full histogram be calculated for each block which comprises the image. This approach also requires that the aforementioned non-linear equalization function be determined and applied for each of the blocks.
Skilled artisans will appreciate that elements in the figures are illustrated for simplicity and clarity and have not necessarily been drawn to scale. For example, the dimensions and/or relative positioning of some of the elements in the figures may be exaggerated relative to other elements to help to improve understanding of various embodiments of the present invention. Also, common but well-understood elements that are useful or necessary in a commercially feasible embodiment are often not depicted in order to facilitate a less obstructed view of these various embodiments of the present invention. It will further be appreciated that certain actions and/or steps may be described or depicted in a particular order of occurrence while those skilled in the art will understand that such specificity with respect to sequence is not actually required. It will also be understood that the terms and expressions used herein have the ordinary technical meaning as is accorded to such terms and expressions by persons skilled in the technical field as set forth above except where different specific meanings have otherwise been set forth herein.